U.S. patent application number 16/742889 was filed with the patent office on 2021-07-15 for system and method for predicting fall armyworm using weather and spatial dynamics.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Sambaran Bandyopadhyay, Sachin Gupta.
Application Number | 20210216861 16/742889 |
Document ID | / |
Family ID | 1000004595692 |
Filed Date | 2021-07-15 |
United States Patent
Application |
20210216861 |
Kind Code |
A1 |
Bandyopadhyay; Sambaran ; et
al. |
July 15, 2021 |
SYSTEM AND METHOD FOR PREDICTING FALL ARMYWORM USING WEATHER AND
SPATIAL DYNAMICS
Abstract
A dynamic graph includes a plurality of nodes and edges at a
plurality of time steps; each node corresponds to a geographic
location in a first area where pest infestation information is
available for a subset of locations. Each edge connects two of the
nodes which are geographically proximate, has a direction based on
wind direction, and has a weight based on relative wind speed.
Assign node features based on weather data as well as labels
corresponding to pest infestation severity. Train a graph
convolutional network on the dynamic graph. Based on predicted
future weather conditions for a second area different than the
first area, use the trained graph convolutional network to predict,
via inductive learning, pest infestation severity for future times
for a new set of nodes corresponding to new geographic locations in
the second area for which no pest infestation information is
available.
Inventors: |
Bandyopadhyay; Sambaran;
(Hooghly, IN) ; Gupta; Sachin; (SINGAPORE,
SG) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
1000004595692 |
Appl. No.: |
16/742889 |
Filed: |
January 14, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/6259 20130101;
G06F 16/29 20190101; G06N 3/08 20130101; G01W 1/10 20130101 |
International
Class: |
G06N 3/08 20060101
G06N003/08; G06F 16/29 20190101 G06F016/29; G01W 1/10 20060101
G01W001/10; G06K 9/62 20060101 G06K009/62 |
Claims
1. A method comprising: creating, in a computer memory, a data
structure representing a dynamic graph including a plurality of
nodes and a plurality of edges connecting said nodes, at a
plurality of time steps, wherein each node corresponds to a
geographic location in a first area, wherein pest infestation
information, relating to an airborne pest whose propagation depends
on weather and wind speed, is available for a subset of said
geographic locations, and wherein each edge: connects two of said
nodes which are geographically proximate; has a direction, for a
given one of said time steps, based on relative wind direction
between said two nodes for said given one of said time steps; and
has a weight, for said given one of said time steps, based on
relative wind speed between said two nodes for said given one of
said time steps; in said data structure, assigning features to each
of said nodes for each of said time steps based on weather data at
said corresponding geographic locations at each of said time steps;
in said data structure, assigning labels to each of said nodes for
each of said time steps, corresponding to pest infestation severity
for each of said time steps, said labels being assigned to those of
said nodes corresponding to said subset of said geographic
locations for which said pest infestation information is available;
training a graph convolutional network on said dynamic graph with
said assigned features and assigned labels; and based on predicted
future weather conditions for a second area different than said
first area, using said trained graph convolutional network to
predict, via inductive learning, pest infestation severity for
future times for a new set of nodes corresponding to new geographic
locations in said second area for which no pest infestation
information is available.
2. The method of claim 1, further comprising initiating at least
one amelioration action based on predicted pest infestation
severity for said future times for said new set of nodes.
3. The method of claim 2, wherein said initiation of said at least
one amelioration action comprises sending an electronic alert to
mobile devices of a plurality of farmers in a region having high
predicted pest infestation severity for said future times for said
new set of nodes.
4. The method of claim 3, further comprising carrying out said at
least one amelioration action.
5. The method of claim 4, wherein said at least one amelioration
action comprises applying a pesticide.
6. The method of claim 4, wherein said at least one amelioration
action comprises planting a trap crop adjacent a valuable crop.
7. The method of claim 4, wherein said at least one amelioration
action comprises deploying natural enemies of said pest.
8. The method of claim 1, wherein said pest infestation information
relates to fall armyworm.
9. The method of claim 1, wherein said weather data includes
temperature, relative humidity, and precipitation.
10. The method of claim 1, wherein connecting said two of said
nodes which are geographically proximate is based on a threshold
distance.
11. A non-transitory computer readable medium comprising computer
executable instructions which when executed by a computer cause the
computer to perform the method of: creating, in a memory of the
computer, a data structure representing a dynamic graph including a
plurality of nodes and a plurality of edges connecting said nodes,
at a plurality of time steps, wherein each node corresponds to a
geographic location in a first area, wherein pest infestation
information, relating to an airborne pest whose propagation depends
on weather and wind speed, is available for a subset of said
geographic locations, and wherein each edge: connects two of said
nodes which are geographically proximate; has a direction, for a
given one of said time steps, based on relative wind direction
between said two nodes for said given one of said time steps; and
has a weight, for said given one of said time steps, based on
relative wind speed between said two nodes for said given one of
said time steps; in said data structure, assigning features to each
of said nodes for each of said time steps based on weather data at
said corresponding geographic locations at each of said time steps;
in said data structure, assigning labels to each of said nodes for
each of said time steps, corresponding to pest infestation severity
for each of said time steps, said labels being assigned to those of
said nodes corresponding to said subset of said geographic
locations for which said pest infestation information is available;
training a graph convolutional network on said dynamic graph with
said assigned features and assigned labels; and based on predicted
future weather conditions for a second area different than said
first area, using said trained graph convolutional network to
predict, via inductive learning, pest infestation severity for
future times for a new set of nodes corresponding to new geographic
locations in said second area for which no pest infestation
information is available.
12. The non-transitory computer readable medium of claim 11,
wherein said method further comprises initiating at least one
amelioration action based on predicted pest infestation severity
for said future times for said new set of nodes.
13. The non-transitory computer readable medium of claim 12,
wherein said initiation of said at least one amelioration action
comprises sending an electronic alert to mobile devices of a
plurality of farmers in a region having high predicted pest
infestation severity for said future times for said new set of
nodes.
14. The non-transitory computer readable medium of claim 11,
wherein said weather data includes temperature, relative humidity,
and precipitation.
15. The non-transitory computer readable medium of claim 11,
wherein connecting said two of said nodes which are geographically
proximate is based on a threshold distance.
16. An apparatus comprising: a memory; and at least one processor,
coupled to said memory, and operative to: create, in said memory, a
data structure representing a dynamic graph including a plurality
of nodes and a plurality of edges connecting said nodes, at a
plurality of time steps, wherein each node corresponds to a
geographic location in a first area, wherein pest infestation
information, relating to an airborne pest whose propagation depends
on weather and wind speed, is available for a subset of said
geographic locations, and wherein each edge: connects two of said
nodes which are geographically proximate; has a direction, for a
given one of said time steps, based on relative wind direction
between said two nodes for said given one of said time steps; and
has a weight, for said given one of said time steps, based on
relative wind speed between said two nodes for said given one of
said time steps; in said data structure, assign features to each of
said nodes for each of said time steps based on weather data at
said corresponding geographic locations at each of said time steps;
in said data structure, assign labels to each of said nodes for
each of said time steps, corresponding to pest infestation severity
for each of said time steps, said labels being assigned to those of
said nodes corresponding to said subset of said geographic
locations for which said pest infestation information is available;
train a graph convolutional network on said dynamic graph with said
assigned features and assigned labels; and based on predicted
future weather conditions for a second area different than said
first area, use said trained graph convolutional network to
predict, via inductive learning, pest infestation severity for
future times for a new set of nodes corresponding to new geographic
locations in said second area for which no pest infestation
information is available.
17. The apparatus of claim 16, wherein said at least one processor
is further operative to initiate at least one amelioration action
based on predicted pest infestation severity for said future times
for said new set of nodes.
18. The apparatus of claim 17, further comprising a network
interface coupled to said at least one processor, wherein said
initiation of said at least one amelioration action comprises said
at least one processor sending, over said network interface, an
electronic alert to mobile devices of a plurality of farmers in a
region having high predicted pest infestation severity for said
future times for said new set of nodes.
19. The apparatus of claim 16, wherein said weather data includes
temperature, relative humidity, and precipitation.
20. The apparatus 16, wherein said at least one processor connects
said two of said nodes which are geographically proximate based on
a threshold distance.
Description
BACKGROUND
[0001] The present invention relates to the electrical, electronic
and computer arts, and more specifically, to application of machine
learning systems to agriculture and the like.
[0002] Fall Armyworm (FAW) is a category of pests that can destroy
a wide variety of crops, which causes large economic damage. FAW is
the larval life stage of a fall armyworm moth. FAW has been
spreading globally in recent years.
[0003] Maize has been a primary target of attack of FAW in some
recently-infected areas. Furthermore, there is also evidence of the
presence of the pest in some other crops in such areas, such as
rice grown in paddies, sugarcane, sweet corn, bajra, jowar and
ragi. Inasmuch as maize is a major ingredient for poultry and
cattle feed, the total loss of FAW indirectly includes the effect
on meat and milk production.
[0004] Prediction of FAW is challenging due to a number of factors.
Learning a generic weather-based model to estimate the ideal
condition(s) for the pest to survive and to create damage is
difficult, as it is very location-specific. Spreading of FAW has a
strong spatial aspect. Since FAW moths can fly fast, the spread of
the disease at the location(s) of interest depends on the current
severity of the pest in neighboring locations. Indeed, the FAW
migration rate is remarkably fast, estimated at 300 miles per
generation. Furthermore, weather factors such as wind speed and
wind direction also play an important role in the spatial spread of
this pest.
SUMMARY
[0005] Principles of the invention provide techniques for
predicting fall armyworm (or similar pests) using weather and
spatial dynamics. In one aspect, an exemplary method includes the
step of creating, in a computer memory, a data structure
representing a dynamic graph including a plurality of nodes and a
plurality of edges connecting the nodes, at a plurality of time
steps, wherein each node corresponds to a geographic location in a
first area, and wherein pest infestation information, relating to
an airborne pest whose propagation depends on weather and wind
speed, is available for a subset of the geographic locations. Each
edge: connects two of the nodes which are geographically proximate;
has a direction, for a given one of the time steps, based on
relative wind direction between the two nodes for the given one of
the time steps; and has a weight, for the given one of the time
steps, based on relative wind speed between the two nodes for the
given one of the time steps. Further steps include, in the data
structure, assigning features to each of the nodes for each of the
time steps based on weather data at the corresponding geographic
locations at each of the time steps; in the data structure,
assigning labels to each of the nodes for each of the time steps,
corresponding to pest infestation severity for each of the time
steps, the labels being assigned to those of the nodes
corresponding to the subset of the geographic locations for which
the pest infestation information is available; training a graph
convolutional network on the dynamic graph with the assigned
features and assigned labels; and, based on predicted future
weather conditions for a second area different than the first area,
using the trained graph convolutional network to predict, via
inductive learning, pest infestation severity for future times for
a new set of nodes corresponding to new geographic locations in the
second area for which no pest infestation information is
available.
[0006] In one aspect, an exemplary apparatus includes a memory; and
at least one processor, coupled to the memory, and operative to
create, in the memory, a data structure representing a dynamic
graph including a plurality of nodes and a plurality of edges
connecting the nodes, at a plurality of time steps, wherein each
node corresponds to a geographic location in a first area, and
wherein pest infestation information, relating to an airborne pest
whose propagation depends on weather and wind speed, is available
for a subset of the geographic locations. Each edge: connects two
of the nodes which are geographically proximate; has a direction,
for a given one of the time steps, based on relative wind direction
between the two nodes for the given one of the time steps; and has
a weight, for the given one of the time steps, based on relative
wind speed between the two nodes for the given one of the time
steps. The at least one processor is further operative to, in the
data structure, assign features to each of the nodes for each of
the time steps based on weather data at the corresponding
geographic locations at each of the time steps; in the data
structure, assign labels to each of the nodes for each of the time
steps, corresponding to pest infestation severity for each of the
time steps, the labels being assigned to those of the nodes
corresponding to the subset of the geographic locations for which
the pest infestation information is available; train a graph
convolutional network on the dynamic graph with the assigned
features and assigned labels; and, based on predicted future
weather conditions for a second area different than the first area,
use the trained graph convolutional network to predict, via
inductive learning, pest infestation severity for future times for
a new set of nodes corresponding to new geographic locations in the
second area for which no pest infestation information is available.
The apparatus optionally has a network interface coupled to the at
least one processor that sends a signal into a communications
network, such as a wireless communications network, to alert
farmers to initiate amelioration action(s) where new infestations
are predicted.
[0007] As used herein, "facilitating" an action includes performing
the action, making the action easier, helping to carry the action
out, or causing the action to be performed. Thus, by way of example
and not limitation, instructions executing on one processor might
facilitate an action carried out by instructions executing on a
remote processor, by sending appropriate data or commands to cause
or aid the action to be performed. For the avoidance of doubt,
where an actor facilitates an action by other than performing the
action, the action is nevertheless performed by some entity or
combination of entities.
[0008] One or more embodiments of the invention or elements thereof
can be implemented in the form of a computer program product
including a computer readable storage medium with computer usable
program code for performing the method steps indicated.
Furthermore, one or more embodiments of the invention or elements
thereof can be implemented in the form of a system (or apparatus)
including a memory, and at least one processor that is coupled to
the memory and operative to perform exemplary method steps. Yet
further, in another aspect, one or more embodiments of the
invention or elements thereof can be implemented in the form of
means for carrying out one or more of the method steps described
herein; the means can include (i) hardware module(s), (ii) software
module(s) stored in a computer readable storage medium (or multiple
such media) and implemented on a hardware processor, or (iii) a
combination of (i) and (ii); any of (i)-(iii) implement the
specific techniques set forth herein.
[0009] Techniques of the present invention can provide substantial
beneficial technical effects. For example, one or more embodiments
provide one or more of:
[0010] a system that considers both spatial dynamics (e.g.,
different locations, their distance, wind speed and direction
between them) and temporal data (e.g., present severity of FAW
attack, different weather parameters over time) of the FAW attack
distribution;
[0011] a method that formulates the problem as attributed dynamic
graphs and uses a graph convolution neural network with an added L2
penalty term (between the parameter matrices of graph convolution
for any two graphs from the consecutive time steps) to ensure
temporal smoothness of the FAW severity;
[0012] a system and method that are inductive in nature, i.e., can
even work for a new set of neighboring locations for which no FAW
attack data is present.
[0013] These and other features and advantages of the present
invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 depicts a cloud computing environment according to an
embodiment of the present invention;
[0015] FIG. 2 depicts abstraction model layers according to an
embodiment of the present invention;
[0016] FIG. 3 is a block diagram of an exemplary system, according
to an aspect of the invention;
[0017] FIG. 4 illustrates conversion of a problem to a dynamic
attributed graph, according to an aspect of the invention;
[0018] FIG. 5 illustrates a graph convolution network (GCN),
according to an aspect of the invention;
[0019] FIG. 6 illustrates equations related to the GCN of FIG. 5,
according to an aspect of the invention;
[0020] FIG. 7 is a flow chart of an exemplary method, according to
an aspect of the invention; and
[0021] FIG. 8 depicts a computer system that may be useful in
implementing one or more aspects and/or elements of the invention,
also representative of a cloud computing node according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0022] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0023] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0024] Characteristics are as follows:
[0025] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0026] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0027] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0028] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0029] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0030] Service Models are as follows:
[0031] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based email). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0032] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0033] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0034] Deployment Models are as follows:
[0035] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0036] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0037] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0038] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load balancing between
clouds).
[0039] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0040] Referring now to FIG. 1, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 1 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0041] Referring now to FIG. 2, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 1) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 2 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0042] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0043] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0044] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0045] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and a
cloud-based service 96 for predicting fall armyworm using weather
and spatial dynamics, it being understood that cloud, non-cloud,
and combined approaches could be employed. For example, any one,
some, or all of the models disclosed herein, once trained, can be
deployed in the cloud.
[0046] One or more embodiments advantageously address the problems
of predicting FAW in a joint framework. Indeed, one or more
embodiments predict/forecast FAW attack at a selected set of
locations, based on: spatial and temporal pattern of the severity
of FAW moths in the set of locations of interest; current weather
condition (e.g., temperature, relative humidity, precipitation) at
all the locations considered; and wind direction and wind speed
between the neighboring locations. Advantageously, one or more
embodiments are capable of predicting/forecasting FAW attack for a
set of locations, some of which already have data/evidence of FAW
attacks (i.e., transductive learning); and/or
predicting/forecasting FAW attack for a set of new locations with
unknown evidence of FAW attacks (inductive learning).
[0047] It is currently believed that the total economic value of
crops that could be affected by FAW is about $1.3 trillion (US);
that about 6% of crops worldwide are damaged by FAW; that the total
value of damaged crops is about $78 billion (US); that about 20% of
at-risk crops could be saved with an effective early alert; and
that, accordingly, there is a potential benefit of $15.6 billion
(US) in effective alert capability.
[0048] One or more embodiments provide a system and/or method for
predicting and forecasting FAW attack severity, over time, for a
set of locations of interest. In a first step, construct a dynamic
graph (i.e., a graph structure where the weights of the edges
change over time) using the appropriate set of locations (to
capture the spatial and dynamic pattern(s) of FAW attack(s)).
Consider each location as a node of the graph. Two locations are
connected by an edge if they are close (within a threshold) to each
other. (This threshold can be determined, for example, by the
physical properties (such as velocity of migration, life cycle,
etc.) of the FAW moths or any other pests, or the threshold can be
tuned based on the historical data of the disease severity.) The
direction (dynamic) of an edge at a time t is formed by the
relative wind direction between the two adjacent nodes, which can
indicate the direction of FAW migration. The weight (dynamic) of an
edge at a time t is formed by the relative wind speed between the
two adjacent nodes, which can indicate the rate of FAW
migration.
[0049] In a second step, assign node features using weather data,
and node labels using existing FAW attack data. Use the set of
weather data (temperature, relative humidity (RH), precipitation)
as the set of features for any node (equivalent to a location). Use
the existing FAW attack severity (1, 2, 3, etc.) at time t as the
labels for a subset of nodes.
[0050] In a third step, train (can also be referred to as "learn")
a Graph Convolution Network (GCN) on the constructed graph. Learn
the parameter(s) of the GCN for the dynamic graphs where some nodes
have labels. Predict the labels (severity of FAW attack at time t)
of other nodes of the dynamic graph using the GCN (transductive
learning).
[0051] In a fourth step, learn the severity of the FAW attack of
other nodes of the dynamic graph where all the nodes are unlabeled.
Use the inductive capability of the learned GCN on the other set of
graphs.
[0052] For FAW attack forecasting, use weather forecast data as the
node features and use the learned GCN to forecast the node
labels.
[0053] Note that the steps are designated first through fourth in
an exemplary embodiment; other embodiments can employ a different
order of steps, subset of steps, superset of steps, and the like,
where appropriate.
[0054] Currently, there are systems available for stacking certain
Cry genes along with Cry 1Fa to result in products that are more
durable and less prone towards FAW attack; there are also systems
available for predicting the migratory routes of FAW and applying
machine learning based techniques, such as neural networks, in
predicting FAW attack(s) using collected data from locally
installed sensors.
[0055] Currently, however, there is no system or methodology
available which predicts the severity of attack of FAW by
converting the problem into a dynamic attributed graph and which
can also be applied where there is no FAW historical data
available.
[0056] As noted above, one or more embodiments provide one or more
of: a system that considers both spatial dynamics (e.g., different
locations, distance between the locations, wind speed and direction
between them) and temporal data (e.g., present severity of FAW
attack, different weather parameters over time) of the FAW attack
distribution; a method that formulates the problem as attributed
dynamic graphs and uses a graph convolution neural network with an
added L2 penalty term (between the parameter matrices of graph
convolution for any two graphs from the consecutive time steps,) to
ensure temporal smoothness of the FAW severity; and a system and
method that are inductive in nature, i.e., can even work for a new
set of neighboring locations for which no FAW attack data is
present.
[0057] Referring to the system diagram of FIG. 3, as seen at 301,
one or more embodiments train on a set of neighboring locations,
where for a subset of the neighboring locations, the severity of
FAW attack is known over time. As seen at 307, convert the problem
to a dynamic attributed graph. Referring also to FIG. 4, given a
set of locations (e.g., districts of a
state/country/continent/geographical region as represented at 301)
and their distances, represent each location by a node 401-1,
401-2, 401-3, 401-4, 401-5, 401-6, 401-7, 401-8 of a graph 403
(there can be any number of nodes; eight is a non-limiting
example). Two nodes are connected by an edge 405 if their distance
is less than some threshold. The wind direction (see block arrow
labeled "wind") between two locations determines the direction of
the edge and the wind speed determines the edge weight. The weather
parameters of a location, such as temperature, relative humidity
and rainfall, can be obtained from weather service 305 and form the
features of the node. Label the nodes with the severity of FAW
attack (label 0 if no FAW occurrence) in the locations
corresponding to the nodes. The node features/labelling can be seen
in the enlargement of node 401-7 indicated by the solid black block
arrow. FIG. 4 is thus a directed attributed graph. Node features;
edge weight, edge direction; and node labels change over time as,
for example, weather parameters and wind can change. Hence, there
will be a different directed attributed graph like FIG. 4 for each
time step; the resultant collection of graphs moving forward in
time is a dynamic attributed directed graph.
[0058] FIG. 5 shows methodological aspects of a graph convolutional
network (GCN). The skilled artisan will be familiar with GCNs from,
for example, Thomas N. Kipf and Max Welling, Semi-supervised
classification with graph convolutional networks, arXiv preprint
arXiv:1609.02907, Sep. 9, 2016, hereby expressly incorporated by
reference herein, in its entirety, for all purposes. In a GCN, each
node aggregates weighted attributes from its neighbors, and updates
its own attributes by a weighted average of its own attributes and
the attributes from its neighbors. This is followed by a non-linear
activation function to update the aggregated features in each node
of the graph. This update can happen over multiple layers to obtain
information from multiple hops of neighbors. Note the input layer
531, hidden layers 533-1, 533-2, and output layer 535. ReLU is the
rectified linear unit (other suitable non-linear activation
functions could be used in other embodiments). In general, a graph
designated as G has vertices V and edges E, takes as input 531 an
N.times. D feature matrix X where N is the number of nodes and D is
the number of input features, as well as an adjacency matrix
(discussed below). The output 535 is an N.times. F feature matrix,
where F is the number of output features per node. Every neural
network layer 533-1, 533-2 is written as a non-linear function
H.sup.(l+1)=f(H.sup.(l), A).
[0059] The parameters (e.g., W matrix) of the GCN neural network
can be updated using a standard back propagation algorithm. Refer
to equations 601 and 603 in FIG. 6. The final set of attributes can
be used to predict the label (i.e., severity of an FAW attack) of a
node. In the equations, j indexes the neighboring nodes of v.sub.i,
and c.sub.ij is a normalization constant for the edge (v.sub.i,
v.sub.j). In particular, A is the adjacency matrix of the graph, A
is the adjacency matrix of the graph after adding self-loop to each
node, {circumflex over (D)} is the diagonal degree matrix of the
graph after adding self-loop to each node, H.sup.(l) is the feature
matrix at layer l of the GCN, W.sup.(l) is the trainable weight
parameters at layer l of the GCN, and h.sub.vi is the row of H
which corresponds to the node v.sub.i in the graph. Note that
.sigma.( ) is a non-linear activation function, of which ReLU is a
non-limiting example.
[0060] Refer now to block 309 in FIG. 3. Consider temporal
constraint via node embedding for the dynamic graph. To incorporate
the dynamic behavior of the graph, one or more embodiments add a
constraint that embedding of a particular node (location) should
not change drastically over the consecutive time steps. This
ensures smoothness on the evolution of the nodes. If h.sub.i.sup.t
is the node embedding of the location i at time t, and similarly
defining h.sub.i.sup.t+1, then minimize
.parallel.h.sub.i.sup.t-h.sub.i.sup.t+1.parallel..sup.2, for all i
and t.
[0061] As seen at 311, train the GCN with added dynamic constraint
via cross entropy and dynamic constraint loss. As seen at 313, 319
it is then possible to predict the labels (FAW severity) at the
unknown nodes (locations) in the graph representing locations
303.
[0062] As seen at 303, it is possible to predict the severity of
FAW where no past information regarding FAW attack is available.
Given a set of new locations 303 (where no FAW attack data is
present), as represented at 315, again build a dynamic attributed
directed network from the distances between the locations 303 and
different weather parameters, including wind information, obtained
from service 305. As seen at 317, use the learned (trained) GCN
model obtained from the process in the top row of FIG. 3. Referring
to 319, as each node has its own attributes from the obtained graph
structure, equation 601 can be used again to compute the node
embeddings and the corresponding severity of attack of FAW for the
new set of locations, with the matrix W fixed in this instance.
[0063] One or more embodiments are directed towards a method and/or
system for predicting Fall Armyworm (FAW) attack severity over time
for a set of locations of interest using weather and spatial
dynamics. One or more embodiments include: constructing a dynamic
graph (i.e., graph structure where weights of the edges change over
time) using the set of locations (to capture the spatial and
dynamic pattern of FAW attack); assigning node features using
weather data and node labels using existing data regarding FAW
attack; training a graph convolution network (GCN) on the
constructed graph and predicting the labels (severity of FAW attack
at time t) of other nodes of the dynamic graph using the GCN
(transductive learning); and learning the severity of FAW attack of
other nodes of the dynamic graph where all the nodes are
unlabeled.
[0064] Heretofore, there have been methods of monitoring fall
armyworm migratory behavior in a region based on changing weather,
historical effect areas, and seasonal patterns, including
predicting attacking severity of fall armyworm over a location
based on collected information and further preventing the spreading
of fall armyworm in new regions. Generally, prior art techniques
model the feasible condition(s) of fall armyworm based on weather,
seasonal patterns and historical effect data and predict the
severity of FAW attack. However, one or more embodiments improve on
the prior art through use of an attributed dynamic graph to capture
the spatial and temporal pattern of the behavior of fall armyworm
in an integrated way. Further, one or more embodiments employ an
inventive use of the graph convolution network (GCN) to predict the
severity of the FAW attack. Yet further, one or more embodiments
can even be applied to a region where no historical data of FAW
attack is present. This is possible by employing the inductive
learning capability, based on GCN, of one or more embodiments.
Indeed, one or more embodiments predict fall armyworm (FAW) attack
severity over time for a set of locations of interest by analyzing
a graph, wherein the graph (i.e., graph structure where weights of
the edges change over time) uses the set of locations to capture
the spatial and dynamic pattern(s) of the FAW attack.
[0065] Again, one or more embodiments construct a dynamic graph
(i.e., graph structure where weights of the edges change over time)
using the set of locations (to capture the spatial and
dynamic/temporal pattern of FAW attack); assign node features using
weather data and node labels using existing data of FAW attack;
learn a Graph Convolution Network (GCN) with an added L2 penalty
term (between the parameter matrices of graph convolution for any
two graphs from the consecutive time steps,) on the constructed
graph and predict the labels (severity of FAW attack at time t) of
other nodes of the dynamic graph using the GCN (transductive
learning); and predict the severity of FAW attack on the nodes of
another dynamic graph where all the nodes are unlabeled (by the
inductive capability (predicting on a completely unseen data)).
[0066] One or more embodiments advantageously capture the dynamic
behavior of the weather conditions between different locations
(such as wind speed, direction etc.) and their relation with the
FAW attack over multiple time intervals, using an attributed
dynamic graph.
[0067] Given the discussion thus far, with continued reference to
FIGS. 3-6, and with reference also now to the flow chart 700 of
FIG. 7, which begins at 701, it will be appreciated that, in
general terms, an exemplary method 700, according to an aspect of
the invention, includes creating, in a computer memory (e.g. 28 in
FIG. 8 discussed below), a data structure representing a dynamic
graph including a plurality of nodes and a plurality of edges
connecting the nodes, at a plurality of time steps, wherein each
node corresponds to a geographic location in a first area. Pest
infestation information, relating to an airborne pest whose
propagation depends on weather and wind speed, is available for a
subset of the geographic locations. Each edge connects two of the
nodes which are geographically proximate; has a direction, for a
given one of the time steps, based on relative wind direction
between the two nodes for the given one of the time steps; and has
a weight, for the given one of the time steps, based on relative
wind speed between the two nodes for the given one of the time
steps. For example, as seen at step 703, create the nodes. Then, as
at steps 704 and 705, loop through certain steps for each pair of
nodes, and for a plurality of time steps. In decision block 707,
determine whether each pair of nodes is geographically proximate
(for example, distance between them does not exceed a threshold
distance). If they are proximate, as per the YES branch, connect
them with an edge at step 709 and proceed to decision block 711;
else, (NO branch) proceed to decision block 711 without connecting
that pair of nodes.
[0068] In decision block 711, determine whether there are
additional pairs of nodes to analyze for proximity; if so (YES
branch), return to step 705; else (NO branch), proceed to step 713.
In step 713, the edge directions and weights are assigned as
discussed. Furthermore, in the data structure, assign features to
each of the nodes for each of the time steps based on weather data
at the corresponding geographic locations at each of the time
steps, and, in the data structure, assigning labels to each of the
nodes for each of the time steps, corresponding to pest infestation
severity for each of the time steps. The labels are assigned to
those of the nodes corresponding to the subset of the geographic
locations for which the pest infestation information is available.
The assigning by time step is seen at decision block 715; if there
are more time steps (YES branch), return to step 704; else (NO
branch), proceed to step 717.
[0069] Step 717 includes training a graph convolutional network on
the dynamic graph with the assigned features and assigned labels.
Step 719 includes, based on predicted future weather conditions for
a second area different than the first area, using the trained
graph convolutional network to predict, via inductive learning,
pest infestation severity for future times for a new set of nodes
corresponding to new geographic locations in the second area for
which no pest infestation information is available. That is to say,
in one or more embodiments, all the new nodes for which prediction
is to be carried out are unlabeled. Block 301 has data about FAW
attack. Use this FAW attack data to train the GCN for the region
corresponding to 301. Then, use the trained GCN on region 303 to
predict FAW severity, even though 303 does not have any FAW attack
data.
[0070] One or more embodiments further include step 721, initiating
at least one amelioration action based on predicted pest
infestation severity for the future times for the new set of nodes.
In a non-limiting example, the initiation of the at least one
amelioration action includes sending an electronic alert to mobile
devices 54A of a plurality of farmers in a region having high
predicted pest infestation severity for the future times for the
new set of nodes (e.g. from a server running service 96 over a
suitable wireless network).
[0071] One or more embodiments further include step 723, carrying
out the at least one amelioration action. For example, famers who
receive the alert apply an environmentally-friendly pesticide;
plant a trap crop (such as Napier grass) adjacent a valuable crop
such as maize; and/or deploy natural enemies of the pest, such as
parasitic wasps, in each case, preferably working in concert with
governmental authorities to assure safety.
[0072] Flow chart 700 ends at 725.
[0073] It should be noted that examples have been provided in the
context of fall armyworm; thus, in one or more embodiments, the
pest infestation information relates to fall armyworm. However,
aspects are generally applicable to pests that depend on weather
conditions, wind speed/direction, and the like. Thus, aspects
disclosed herein can be generalized by the skilled artisan to other
pests or diseases which can spread spatially with wind speed and so
on.
[0074] In one or more embodiments, the weather data includes
temperature, relative humidity, and precipitation. The skilled
artisan will appreciate that a psychrometric chart or the like can
be used to relate dry bulb temperature, wet bulb temperature,
relative humidity, specific humidity, and actual humidity; suitable
conversions can be carried out to obtain desired data when
different weather data than that desired is available.
[0075] In one or more embodiments, connecting the two of the nodes
which are geographically proximate is based on a threshold
distance.
[0076] In another aspect, referring to FIG. 8, discussed further
below, an exemplary apparatus includes a memory 28, and at least
one processor 16, coupled to the memory, and operative to carry out
or otherwise facilitate any one, some, or all of the method steps
described herein. Some embodiments further include a network
interface 20 coupled to the at least one processor; the initiation
of the at least one amelioration action includes the at least one
processor sending, over the network interface, an electronic alert
to mobile devices 54A of a plurality of farmers in a region having
high predicted pest infestation severity for the future times for
the new set of nodes.
[0077] One or more embodiments of the invention, or elements
thereof, can be implemented in the form of an apparatus including a
memory and at least one processor that is coupled to the memory and
operative to perform exemplary method steps. FIG. 8 depicts a
computer system that may be useful in implementing one or more
aspects and/or elements of the invention, also representative of a
cloud computing node according to an embodiment of the present
invention. Referring now to FIG. 8, cloud computing node 10 is only
one example of a suitable cloud computing node and is not intended
to suggest any limitation as to the scope of use or functionality
of embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0078] In cloud computing node 10 there is a computer system/server
12, which is operational with numerous other general purpose or
special purpose computing system environments or configurations.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
handheld or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0079] Computer system/server 12 may be described in the general
context of computer system executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0080] As shown in FIG. 8, computer system/server 12 in cloud
computing node 10 is shown in the form of a general-purpose
computing device. The components of computer system/server 12 may
include, but are not limited to, one or more processors or
processing units 16, a system memory 28, and a bus 18 that couples
various system components including system memory 28 to processor
16.
[0081] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component Interconnect
(PCI) bus.
[0082] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0083] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0084] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0085] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, and
external disk drive arrays, RAID systems, tape drives, and data
archival storage systems, etc.
[0086] Thus, one or more embodiments can make use of software
running on a general purpose computer or workstation. With
reference to FIG. 8, such an implementation might employ, for
example, a processor 16, a memory 28, and an input/output interface
22 to a display 24 and external device(s) 14 such as a keyboard, a
pointing device, or the like. The term "processor" as used herein
is intended to include any processing device, such as, for example,
one that includes a CPU (central processing unit) and/or other
forms of processing circuitry. Further, the term "processor" may
refer to more than one individual processor. The term "memory" is
intended to include memory associated with a processor or CPU, such
as, for example, RAM (random access memory) 30, ROM (read only
memory), a fixed memory device (for example, hard drive 34), a
removable memory device (for example, diskette), a flash memory and
the like. In addition, the phrase "input/output interface" as used
herein, is intended to contemplate an interface to, for example,
one or more mechanisms for inputting data to the processing unit
(for example, mouse), and one or more mechanisms for providing
results associated with the processing unit (for example, printer).
The processor 16, memory 28, and input/output interface 22 can be
interconnected, for example, via bus 18 as part of a data
processing unit 12. Suitable interconnections, for example via bus
18, can also be provided to a network interface 20, such as a
network card, which can be provided to interface with a computer
network, and to a media interface, such as a diskette or CD-ROM
drive, which can be provided to interface with suitable media.
[0087] Accordingly, computer software including instructions or
code for performing the methodologies of the invention, as
described herein, may be stored in one or more of the associated
memory devices (for example, ROM, fixed or removable memory) and,
when ready to be utilized, loaded in part or in whole (for example,
into RAM) and implemented by a CPU. Such software could include,
but is not limited to, firmware, resident software, microcode, and
the like.
[0088] A data processing system suitable for storing and/or
executing program code will include at least one processor 16
coupled directly or indirectly to memory elements 28 through a
system bus 18. The memory elements can include local memory
employed during actual implementation of the program code, bulk
storage, and cache memories 32 which provide temporary storage of
at least some program code in order to reduce the number of times
code must be retrieved from bulk storage during implementation.
[0089] Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, and the like) can be coupled
to the system either directly or through intervening I/O
controllers.
[0090] Network adapters 20 may also be coupled to the system to
enable the data processing system to become coupled to other data
processing systems or remote printers or storage devices through
intervening private or public networks. Modems, cable modem and
Ethernet cards are just a few of the currently available types of
network adapters.
[0091] As used herein, including the claims, a "server" includes a
physical data processing system (for example, system 12 as shown in
FIG. 8) running a server program. It will be understood that such a
physical server may or may not include a display and keyboard.
[0092] One or more embodiments can be at least partially
implemented in the context of a cloud or virtual machine
environment, although this is exemplary and non-limiting. Reference
is made back to FIGS. 1-2 and accompanying text. Consider, e.g., a
cloud-based service 96 for predicting fall armyworm using weather
and spatial dynamics, located in layer 90.
[0093] It should be noted that any of the methods described herein
can include an additional step of providing a system comprising
distinct software modules embodied on a computer readable storage
medium; the modules can include, for example, any or all of the
appropriate elements depicted in the block diagrams and/or
described herein; by way of example and not limitation, any one,
some or all of the modules/blocks and or sub-modules/sub-blocks
described. The method steps can then be carried out using the
distinct software modules and/or sub-modules of the system, as
described above, executing on one or more hardware processors such
as 16. Further, a computer program product can include a
computer-readable storage medium with code adapted to be
implemented to carry out one or more method steps described herein,
including the provision of the system with the distinct software
modules.
[0094] One example of user interface that could be employed in some
cases is hypertext markup language (HTML) code served out by a
server or the like, to a browser of a computing device of a user.
The HTML is parsed by the browser on the user's computing device to
create a graphical user interface (GUI).
[0095] Exemplary System and Article of Manufacture Details
[0096] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0097] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0098] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0099] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0100] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0101] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0102] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0103] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0104] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
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